
fj
as supplemental elements.This function computes a bootstrap resampled set of data and projects
fj
as supplemental elements.
boot.compute.fj(DATA, res, DESIGN = NULL, constrained = FALSE)
a set of factor scores of the measures (columns,
fj
) for the bootstrapped data.
The original data matrix to be bootstrapped. Rows will be bootstrapped and are assumed to be observations.
of class expoOutput
. Results from one of the
ExPosition
methods (e.g., epPCA
,
epMCA
),
A design matrix (in disjunctive coding). Only used if
constrained
is TRUE.
a boolean. If TRUE, bootstrap resampling will occur
within groups as designated by the DESIGN
matrix.
Derek Beaton
Chernick, M. R. (2008). Bootstrap methods: A guide for
practitioners and researchers (Vol. 619). Wiley-Interscience.
Hesterberg, T. (2011). Bootstrap. Wiley Interdisciplinary Reviews:
Computational Statistics, 3, 497–526.
See the functions supplementaryCols
and
link{boot.samples}
##the following code generates 100 bootstrap resampled
##projections of the measures from the Iris data set.
data(ep.iris)
data <- ep.iris$data
design <- ep.iris$design
iris.pca <- epPCA(data,scale="SS1",DESIGN=design,make_design_nominal=FALSE)
boot.fjs.unconstrained <- array(0,dim=c(dim(iris.pca$ExPosition.Data$fj),100))
boot.fjs.constrained <- array(0,dim=c(dim(iris.pca$ExPosition.Data$fj),100))
for(i in 1:100){
#unconstrained means we resample any of the 150 flowers
boot.fjs.unconstrained[,,i] <- boot.compute.fj(ep.iris$data,iris.pca)
#constrained resamples within each of the 3 groups
boot.fjs.constrained[,,i] <- boot.compute.fj(data,iris.pca,design,TRUE)
}
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